Systems and methods to classify a road based on a level of suppport offered by the road for autonomous driving operations

ABSTRACT

The disclosure generally pertains to systems and methods to classify a road based on a level of support offered by the road for autonomous driving operations. An example method may involve a computer receiving sensor data from a vehicle, the sensor data containing information about a current functional condition of a road. The computer may predict a future functional condition of the road by using a deterioration model to evaluate the sensor data. The computer may then determine a level of support offered by the road for autonomous driving operations based on the future functional condition of the road, and assign a classification to the road based on the level of support offered by the road for autonomous driving operations. The level of support offered by the road for autonomous driving operations may also be based on items such as road markings, traffic signs, traffic signals, and/or infrastructure elements.

BACKGROUND

Deployment of an autonomous vehicle on a road not only depends on theequipment provided in the autonomous vehicle but also on thecharacteristics of the road and of various road markings, traffic signs,and traffic lights that are used by the autonomous vehicle. Some itemssuch as, for example, painted lane markings on the road, or a surfacematerial of the road, can deteriorate over time. It may therefore bedifficult to determine ahead of time whether a road is optimal fortravel by an autonomous vehicle. Consequently, it is desirable toprovide a solution to address this issue.

DESCRIPTION OF THE FIGURES

The detailed description is set forth with reference to the accompanyingdrawings. The use of the same reference numerals may indicate similar oridentical items. Various embodiments may utilize elements and/orcomponents other than those illustrated in the drawings, and someelements and/or components may not be present in various embodiments.Elements and/or components in the figures are not necessarily drawn toscale. Throughout this disclosure, depending on the context, singularand plural terminology may be used interchangeably.

FIG. 1 shows an example system that can be used operations in accordancewith an embodiment of the disclosure to classify a road based on a levelof support offered by the road for autonomous driving operations.

FIG. 2 shows a block diagram of various inputs that can be provided to aroad classification system computer in accordance with an embodiment ofthe disclosure.

FIG. 3 shows an example table illustrating various levels that may beused to characterize deterioration of an object in accordance with anembodiment of the disclosure.

FIG. 4 shows an example table illustrating parameters that may be usedto determine a viability of a road to support autonomous vehicleoperations in accordance with an embodiment of the disclosure.

FIG. 5 shows another example table illustrating parameters that may beused to determine a viability of a road to support autonomous vehicleoperations in accordance with an embodiment of the disclosure.

DETAILED DESCRIPTION Overview

The disclosure generally pertains to systems and methods to classify aroad based on a level of support offered by the road for autonomousdriving operations. An example method for classifying a road may involvea computer receiving sensor data from a vehicle. The sensor data maycontain information about a current functional condition of a road. Thecomputer may predict a future functional condition of the road by usinga deterioration model to evaluate the sensor data. In one case, thedeterioration model may be based on an effect of an environmentalcondition upon a road surface over a period of time. The computer maydetermine a level of support offered by the road for autonomous drivingoperations based on the future functional condition of the road, andthen assign a classification to the road based on the level of supportoffered by the road. The level of support offered by the road forautonomous driving operations may be also based on items such as, forexample, road markings, traffic signs, traffic signals, and/orinfrastructure elements. Based on the level of support offered by theroad (or portion of the road), the system may autonomously reroute thevehicle to a different road that has a more optical level of support forautonomous driving operations.

Illustrative Embodiments

The disclosure will be described more fully hereinafter with referenceto the accompanying drawings, in which example embodiments of thedisclosure are shown. This disclosure may, however, be embodied in manydifferent forms and should not be construed as limited to the exampleembodiments set forth herein. It will be apparent to persons skilled inthe relevant art that various changes in form and detail can be made tovarious embodiments without departing from the spirit and scope of thepresent disclosure. Thus, the breadth and scope of the presentdisclosure should not be limited by any of the above-described exampleembodiments but should be defined only in accordance with the followingclaims and their equivalents. The description below has been presentedfor the purposes of illustration and is not intended to be exhaustive orto be limited to the precise form disclosed. It should be understoodthat alternate implementations may be used in any combination desired toform additional hybrid implementations of the present disclosure. Forexample, any of the functionalities described with respect to aparticular device or component may be performed by another device orcomponent. Furthermore, while specific device characteristics have beendescribed, embodiments of the disclosure may relate to numerous otherdevice characteristics. Further, although embodiments have beendescribed in language specific to structural features and/ormethodological acts, it is to be understood that the disclosure is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as illustrative forms ofimplementing the embodiments.

Certain words and phrases are used herein solely for convenience andsuch words and terms should be interpreted as referring to variousobjects and actions that are generally understood in various forms andequivalencies by persons of ordinary skill in the art. The word“example” as used herein is intended to be non-exclusionary andnon-limiting in nature.

FIG. 1 shows an example system 100 that can be used operations inaccordance with an embodiment of the disclosure to classify a road 150based on a level of support offered by the road 150 for autonomousdriving. The system 100 may include a road classification system 145, avehicle records database 160, and a public records database 170.

The road classification system 145 may include one or more computerscommunicatively coupled to a network 110, such as, for example, acomputer 146 that is communicatively coupled to the network 110. Each ofthe vehicle records database 160 and the public records database 170 mayalso include one or more computers (not shown) that are communicativelycoupled to the network 110. The various computers may be any of varioustypes of computers containing a processor and a memory, such as, forexample, a desktop computer, a laptop computer, a tablet computer, aserver computer, a client computer, or a handheld device (a smartphone,for example).

The network 110 may include any one network, or a combination ofnetworks, such as, for example, a local area network (LAN), a wide areanetwork (WAN), a telephone network, a cellular network, a cable network,a wireless network, and/or private/public networks such as the Internet.The various components that are communicatively coupled to the network110 may communicate with each other by using various communicationtechnologies such as, for example, TCP/IP, Bluetooth, cellular,near-field communication (NFC), Wi-Fi, Wi-Fi direct, vehicle-to-vehicle(V2V) communication, and/or vehicle-to-infrastructure (V2I)communication.

A vehicle 120 and a vehicle 130 are two example vehicles shown travelingon the road 150. Each of the vehicle 120 and the vehicle 130 can be anyof various types of vehicles, such as, for example, a truck, asemi-trailer, a flatbed, a car, a van, a sports utility vehicle, and abus. In an example embodiment, the vehicle 120 and/or the vehicle 130 isan autonomous vehicle. It must be understood that the label “autonomousvehicle” as used in this disclosure generally refers to a vehicle thatcan perform at least a few operations without human intervention.

The Society of Automotive Engineers (SAE) defines six levels of drivingautomation ranging from Level 0 (fully manual) to Level 5 (fullyautonomous). These levels have been adopted by the U.S. Department ofTransportation. Level 0 (L0) vehicles are manually controlled vehicleshaving no driving related automation. Level 1 (L1) vehicles incorporatesome features, such as cruise control, but a human driver retainscontrol of most driving and maneuvering operations. Level 2 (L2)vehicles are partially automated with certain driving operations such assteering, braking, and lane control being controlled by a vehiclecomputer. The driver retains some level of control of the vehicle andmay override certain operations executed by the vehicle computer. Level3 (L3) vehicles provide conditional driving automation but are smarterin terms of having an ability to sense a driving environment and certaindriving situations. Level 4 (L4) vehicles can operate in a self-drivingmode and include features where the vehicle computer takes controlduring certain types of equipment failures. The level of humanintervention is very low. Level 5 (L5) vehicles are fully autonomousvehicles that do not involve human participation.

The description provided herein with reference to an “autonomousvehicle” is applicable to Level 2 through Level 5 vehicles and may beapplicable to certain types of Level 0 and Level 1 vehicles as well. TheLevel 0 and Level 1 vehicles may be equipped with sensors that arearranged to detect functional conditions of the road 150 and/or ofvarious objects (such as, for example, traffic signs and trafficsignals) that are pertinent to usage of the road 150 by autonomousvehicles (Level 2 through Level 5).

The vehicle 120 is generally configured to have an autonomous drivingcapability and can include equipment such as, for example, a computer121 and a sensor system 122. The computer 121 may include acommunication system that allows the computer 121 to communicate withvarious devices either via the network 110 (such as, for example, thecomputer 146) or directly via vehicle-to-vehicle (V2V) wirelesscommunications (such as, for example, with a computer 131 of the vehicle130 via a V2V wireless communication link 134).

In an example implementation, the computer 121 is configured to executeoperations such as receiving sensor data from the sensor system 122,embedding the sensor data in a communication signal, and propagating thecommunication signal containing the sensor data to devices such as thecomputer 146. The computer 146 is configured in accordance withdisclosure to execute a software-based classification procedure toevaluate the sensor data to determine a level of support provided by theroad 150 for autonomous driving operations.

In another example implementation, the computer 121 is configured inaccordance with disclosure to execute the software-based classificationprocedure to evaluate the sensor data and determine the level of supportprovided by the road 150 for autonomous driving operations. The resultsof the classification procedure may then be propagated to the computer146 and/or to the public records database 170.

The sensor system 122 may include various sensor devices such as forexample, a camera 123, a radar device, a light detection and ranging(LIDAR) device, and an Internet-of-Things (IoT) sensor. The sensordevices are arranged to capture data that provides informationpertaining to a functional condition of the road 150 and/or to obtaininformation pertaining to various objects that support autonomousdriving operations.

In an example implementation, data provided by the camera 123, in theform of images of the road 150, may be evaluated by the computer 146and/or the computer 121 for obtaining information pertaining to thefunctional condition of the road 150 (smooth, bumpy, pot-holed, slick,cement, tarmac, asphalt, gravel, dirt, cobblestones, etc.). Dataprovided by the camera 123, in the form of images of various objects,may be evaluated by the computer 146 and/or the computer 121 forobtaining information pertaining to, for example, painted markings,reflectors, median areas, white lines, yellow lines, dashed lines, solidlines, turn lanes, road dividers, shoulders, exit lanes, exit signs,pedestrian crossing markings, pedestrian crossing signs, traffic signs,traffic signals and/or infrastructure elements that support autonomousdriving operations.

In an example embodiment in accordance with the disclosure, the vehicle130 may be configured substantially similar to the vehicle 120.Accordingly, the computer 131 may be substantially similar to thecomputer 121 and the sensor system 132 may be substantially similar tothe sensor system 122 (the camera 133 may be substantially similar tothe camera 133).

The vehicle records database 160 can contain data that provides varioustypes of information pertaining to various vehicles (including thevehicle 120 and the vehicle 130). The data may provide, for example,information pertaining to vehicle ownership, vehicle maintenance, andaccidents. Such information may be fetched by the computer 146 and/orother computers, and used to determine a road-worthiness of a vehicle.In an example scenario, the computer 146 may access the vehicle recordsdatabase 160 to obtain data pertaining to the vehicle 120. The data maybe evaluated by the computer 146 to identify features and capabilitiesof the vehicle 120 such as, for example, an autonomous drivingcapability of the vehicle 120. The computer 146 may, for example,identify the vehicle 120 as a Level 5 autonomous vehicle that has beenmaintained in excellent condition over a period of time (5 years, forexample) or as a Level 1 vehicle that has been poorly maintained.

The public records database 170 can contain data that provides varioustypes of information pertaining to various structures such as roads andhighways. The public records database 170 can contain, for example,records of roadwork performed on the road 150 (painting, repaving,pothole filling, adding lanes, re-routing, additional construction,etc.) and records pertaining to infrastructure objects associated withthe road 150 such as, for example traffic signs and traffic lights(addition, removal, maintenance, etc.). Data obtained from the publicrecords database 170 may be evaluated by the computer 146, and/or othercomputers, to determine a road-worthiness of a road, particularly withreference to support offered by the road for autonomous drivingoperations. In an example scenario, the computer 146 may access thepublic records database 170 to obtain data pertaining to the road 150.The computer 146 may evaluate the data and classify the road 150 ascapable of supporting certain types of autonomous driving operations.

FIG. 2 shows a block diagram of various inputs that can be provided to aroad classification system computer such as, for example, the computer146, in accordance with an embodiment of the disclosure. The variousinputs can include road-related vehicle sensor data 205, road-relatedcrowd sourced data 210, road-related records data 215, vehicle featuresdata 220, road features data 225, road cartographic data 240,environmental data 245, and a deterioration model 250. The computer 146may evaluate some or all of these inputs to produce classificationresults 235 that may include a classification of a road (such as, theroad 150) based on a level of support offered by the road for autonomousdriving operations.

The classification results 235 may further provide information relatedto various items (particularly with respect to autonomous drivingoperations) such as, for example, evaluating a current functionalcondition of a road, predicting a future functional condition of a road,predicting a rate of deterioration of a road over time, identifying acause for a deterioration of a road, recommending a repair of a road toaddress a current and/or a future deterioration of the road, identifyingan issue with an infrastructure object (a traffic light, for example),recommending a way to address the issue.

The road-related vehicle sensor data 205 may be provided to the computer146 by various sources such as, for example, the sensor system 122 (viathe computer 121) and/or the sensor system 132 (via the computer 131).

Road-related crowd sourced data 210 may be provided to the computer 146by sources such as, for example, vehicle computers that are incommunication with each other and/or vehicle occupants (drivers and/orpassengers) that are in communication with each other. In an examplescenario, the computer 121 may obtain road-related information from thecomputer 131 and propagate the road-related information to the computer146. The road-related information obtained by the computer 121 from thecomputer 131 (such as, for example, sensor data generated by the sensorsystem 132) may be different than road-related information obtained bythe computer 131 from the sensor system 122. The sensor data may bedifferent for various reasons such as, for example, because the vehicle130 is traveling on a first segment of the road 150 and the vehicle 120is traveling on a second segment of the road 150 where the roadconditions and/or environmental conditions are different. In anotherexample scenario, the computer 121 may obtain road-related informationfrom vehicle occupants such as, for example, road-related informationpertaining to road conditions and/or environmental conditions (rain, icyweather, snow etc.) on the first segment of the road 150 as reported tothe computer 146 by a driver of the vehicle 120 (via the computer 121 orvia a smartphone, for example) that may be different than roadconditions and/or environmental conditions on the second segment of theroad 150 as reported to the computer 146 by a driver of the vehicle 130.

The vehicle features data 220, the road-related records data 215, theroad features data 225, the road cartographic data 240, and/or theenvironmental data 245 may be obtained by the computer 146 from sourcessuch as the vehicle records database 160 and the public records database170. The vehicle features data 220 may provide information pertaining toan autonomous driving capability of a vehicle. The road-related recordsdata 215 may provide information pertaining to a current functionalcondition of a road and roadwork performed on the road (painting,repaving, pothole filling, adding lanes, re-routing, additionalconstruction, etc.). The road features data 225 may provide informationpertaining to features of a road such as, for example, terrain and shape(hilly, flat, curvy, etc.). The road cartographic data 240 may includemap information pertaining to a road such as, for example, a route thatextends from a first geographic region having a first type of weather(hot, for example) to a second geographic region having a second type ofweather (snowy, for example) during various times of the year.Environmental data 245 may include information pertaining to the weatherat various locations on a road at various times.

The computer 146 may include various components such as, for example, aprocessor, a communications system, and a memory. The communicationssystem is configured to allow the computer 146 to communicate withvarious devices, such as, for example, the computer 121 in the vehicle120, the computer 131 in the vehicle 130, the vehicle records database160, and/or the vehicle records database 160. The memory is one exampleof a non-transitory computer-readable medium that may be used to storean operating system and various code modules such as, for example, aroad classification module. The code modules are provided in the form ofcomputer-executable instructions that can be executed by the processorfor performing various operations in accordance with the disclosure.

The road classification module may be executed by the processor toperform a prediction procedure that predicts a future functionalcondition of a road based on evaluating sensor data pertaining to acurrent functional condition of the road and/or based on other inputssuch as, for example, an effect of an environmental condition upon aroad surface over a period of time. The prediction procedure may use thedeterioration model 250 that can include techniques/algorithms based onartificial intelligence, Markov decision processes, reinforcementlearning, and learning based on past states. Some or all of theroad-related vehicle sensor data 205, road-related crowd sourced data210, road-related records data 215, vehicle features data 220, roadfeatures data 225, road cartographic data 240, and/or environmental data245 may be evaluated individually, or in combined form, to classify someor all portions of a road. In some cases, a first segment of the road150 may be assigned a first type of classification based on a level ofsupport offered by the first segment for autonomous driving operation. Asecond segment of the road 150 may be assigned a second type ofclassification based on a different level of support offered by thesecond segment for autonomous driving operation.

In an example implementation, the prediction procedure may be executedin the form of a simulation procedure where various inputs are based onsimulation values. In some cases, the simulation values may be based onhistorical data obtained from the vehicle records database 160 and/orthe public records database 170. In some other cases, the simulationprocedure can use data obtained from a vehicle computer, such as, forexample, the computer 121.

In some cases, the classification of various road segments may beupdated in real time based on changing conditions, such as, for example,changing weather conditions. In some other cases, the classification maybe updated periodically, intermittently, and/or on as as-needed basis.In an example implementation, an aggregated autonomous driving index maybe generated by the computer 146 for the road 150 based on theclassification of various segments of the road 150. In an exampleimplementation, the classification results 235 may be propagated to, andstored in, the public records database 170.

An autonomous vehicle may communicate with the computer 146 and/or thepublic records database 170 to obtain classification information aboutone or more road segments and use the classification information toplan, execute, and/or modify a travel route of the autonomous vehicle.Various other entities, such as, for example, a government agency maycommunicate with the computer 146 and/or the public records database 170to obtain classification information about one or more road segments anduse the classification information to plan, modify, and/or repairvarious segments of the road 150.

FIG. 3 shows an example table 300 illustrating various levels that maybe used to characterize deterioration of an object in accordance with anembodiment of the disclosure. The object can be, for example, anautonomous vehicle, a road, a road marking (a lane marking, forexample), a traffic sign (a painted turn signal board, for example), oran infrastructure object (a traffic light or a roadside IoT equipment,for example). In this example embodiment, seven deterioration levels areshown in the table 300 ranging from Level 1 to Level 7 with increasinglevels indicating a greater extent of damage. In other implementations,the deterioration of an object may be characterized by fewer, or more,number of levels, and the extent of damage may be indicated in otherways such as, for example, increasing levels indicating a lesser extentof damage.

In the example table 300, Level 1 indicates no damage and Level 7indicates a level of damage that renders the object unusable. Thus, forexample, an autonomous vehicle may be usable on all roads when theautonomous vehicle is characterized by a Level 1 deterioration level andunusable for travel on all roads when the autonomous vehicle ischaracterized by a Level 7 deterioration level. Level 2 (nano damage)indicates a level of deterioration that may be undetectable by the humaneye but may be detectable by some instruments. Level 3 (micro damage)indicates a level of deterioration that may be detectable by the humaneye to some degree but can have an impact upon another object (such as,for example, a road having a Level 3 deterioration may have some adverseimpact upon an autonomous vehicle traveling on the road). Level 4 (minordamage) indicates a level of deterioration that is detectable by thehuman eye and can be rectified (such as, for example, a road havingpotholes that can be filled). Level 5 (moderate damage) indicates alevel of deterioration that is detectable by the human eye and can berectified with a significant amount of work (such as, for example, aroad that requires re-paving). Level 6 (major damage) indicates a levelof deterioration that renders the object unreliable for use (such as,for example, a road having potholes that are very large and may fill upwith water during rainfall). Level 7 (unusable) indicates a level ofdeterioration that renders the object completely unusable (such as, forexample, a road that includes a bridge that has collapsed).

FIG. 4 shows an example table 400 illustrating some example parametersthat may be used to determine a viability of a road to supportautonomous vehicle operations in accordance with an embodiment of thedisclosure. Column 405 of the table 400 indicates an autonomous drivingcapability of a vehicle. The autonomous driving capability of a vehiclemay be characterized in various ways such as, for example, by using thesix levels of automation defined by the Society of Automotive Engineers(SAE) (Level 0 (fully manual) through Level 5 (fully autonomous)). Inanother implementation, column 405 of the table 400 can include a set ofcomponents that provides an indication of an autonomous drivingcapability of a vehicle, such as, for example various devices of asensor system used by the vehicle for autonomous driving.

Column 410 contains an example list of road features that supportautonomous driving operations. Column 415 indicates a capability of avehicle (indicated in terms of deterioration levels) with respect to thevarious road features listed in column 410. Column 420 indicates acurrent functional condition of the road (indicated in terms ofdeterioration levels) with respect to the various road features listedin column 410. Column 425 indicates a viability of the road forautonomous driving operations based on evaluating a compatibilitybetween the current functional condition of the road and the conditionof the vehicle with respect to the various road features.

In the example scenario illustrated in table 400, the road is deemedviable for autonomous driving operations in terms of road surfaceconditions, based on the road having a road surface conditioncorresponding to Level 2 (nano damage) and the vehicle having acondition corresponding to Level 5 (moderate damage). The road surfacecondition of the road is better than a threshold road surface conditionrequired for use of the road by the vehicle in this instance. Moreparticularly, the threshold road surface condition requirement in thiscase is Level 5 (moderate damage), corresponding to the deteriorationlevel of the vehicle (Level 5). A road having a poorer deteriorationlevel in terms of road surface conditions, such as, for example, Level 6(major damage) or Level 7 (unusable) would render the road unviable foruse by the vehicle.

In the example scenario illustrated in table 400, the road is deemedviable for autonomous driving operations in terms of road markings,based on the road having current road markings corresponding to Level 2(nano damage) and the vehicle having a condition corresponding to Level2 (nano damage). The threshold road markings requirement in this casecorresponds to a Level 2 deterioration level. A road having a poorerdeterioration level such as, for example, Level 3 (micro damage) orLevel 6 (major damage), would render the road unviable for use by thevehicle due to various reasons, such as, for example, because travel onthe road may potentially cause damage to the vehicle.

In the example scenario illustrated in table 400, the road is deemedviable for autonomous driving operations in terms of traffic signs,based on the road having traffic signs corresponding to Level 1 (nodamage) and the vehicle having a condition corresponding to Level 2(nano damage). The threshold traffic signs requirement in this casecorresponds to a Level 2 deterioration level. A road having trafficsigns with a poorer deterioration level such as, for example, Level 6(major damage), would render the road unviable for use by the vehicledue to various reasons, such as, for example, because the traffic signs(a painted turn signal board, for example) would be sub-standard for useby the automation provided in the vehicle.

In the example scenario illustrated in table 400, the road is deemedviable for autonomous driving operations in terms of traffic signals,based on the road having traffic signals corresponding to Level 1 (nodamage) and the vehicle having a condition corresponding to Level 2(nano damage). The threshold traffic signals requirement in this casecorresponds to a Level 2 deterioration level. A road having trafficsignals with a poorer deterioration level such as, for example, Level 6(major damage), would render the road unviable for use by the vehicledue to various reasons, such as, for example, because the trafficsignals would be sub-standard for use by the automation provided in thevehicle.

FIG. 5 shows an example table 500 illustrating some example parametersthat may be used to determine a viability of a road to supportautonomous vehicle operations in the future in accordance with anembodiment of the disclosure. The example table 500 includes column 505that is identical to column 405 of the table 400, column 510 that isidentical to column 510 of the table 400, column 515 that is slightlymodified version of column 415 of the table 400 (one entry correspondingto road surface conditions is changed from Level 5 to Level 3), column520 that is identical to column 420 of the table 400, column 525 thatindicates a future functional condition of a road (indicated in terms ofdeterioration levels) with respect to the various road features listedin column 510, and column 530 that indicates a viability of the road tosupport autonomous vehicle operations in the future based on evaluatinga compatibility between the future functional condition of the road andthe condition of the vehicle with respect to the various road features.

The future functional condition of the road (column 525) may be obtainedby executing a prediction procedure in the computer 146 in the mannerdescribed above with reference to FIG. 2, for example. In an exampleimplementation, the prediction procedure for predicting the futurefunctional condition of the road may be based on the current functionalcondition of the road indicated in column 520. The current functionalcondition of the road can correspond to a first instant in time and thefuture functional condition of the road can correspond to a secondinstant in time that occurs subsequent to the first instant in time. Inan example scenario, the first instant in time can correspond to a timeof execution of the prediction procedure and the second instant in timecan correspond to a subsequent time such as, for example, several yearslater.

In the example scenario illustrated in table 500, the road is deemed“not viable” (unviable) for future autonomous driving operations interms of road surface conditions, based on the road having a roadsurface condition that is predicted to deteriorate from a Level 2 (nanodamage) to a Level 4 (minor damage) over time and the vehicle having acondition corresponding to Level 3 (micro damage). The road surfacecondition of the road is lower than a threshold road surface conditionrequired for use of the road by the vehicle in this instance. Moreparticularly, the threshold road surface condition requirement in thiscase corresponds to the Level 3 deterioration level of the vehicle. Aroad having a poorer deterioration level in terms of road surfaceconditions, such as, for example, Level 4 (minor damage) renders theroad unviable for use by the vehicle.

The road is deemed unviable for autonomous driving operations in termsof road markings, based on the road having a road surface condition thatis predicted to deteriorate from a Level 2 (nano damage) to a Level 4(minor damage) over time and the vehicle having a conditioncorresponding to Level 2 (nano damage). The road surface condition ofthe road is lower than a threshold road markings requirement for use ofthe road by the vehicle in this instance. More particularly, thethreshold road surface condition requirement in this case corresponds tothe Level 2 deterioration level of the vehicle. A road having a poorerdeterioration level such as, for example, Level 4 (minor damage) rendersthe road unviable for use by the vehicle due to various reasons, suchas, for example, because travel on the road may potentially cause damageto the vehicle.

The future functional condition of the road with respect to trafficsigns, traffic signals, and infrastructure objects remains unchangedwith respect to the current functional condition of the road.Consequently, the road is viable for autonomous driving operations inthese categories.

In the above disclosure, reference has been made to the accompanyingdrawings, which form a part hereof, which illustrate specificimplementations in which the present disclosure may be practiced. It isunderstood that other implementations may be utilized, and structuralchanges may be made without departing from the scope of the presentdisclosure. References in the specification to “one embodiment,” “anembodiment,” “an example embodiment,” “an example embodiment,” etc.,indicate that the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, one skilled in the art willrecognize such feature, structure, or characteristic in connection withother embodiments whether or not explicitly described.

Implementations of the systems, apparatuses, devices, and methodsdisclosed herein may comprise or utilize one or more devices thatinclude hardware, such as, for example, one or more processors andsystem memory, as discussed herein. An implementation of the devices,systems, and methods disclosed herein may communicate over a computernetwork. A “network” is defined as one or more data links that enablethe transport of electronic data between computer systems and/or modulesand/or other electronic devices. When information is transferred orprovided over a network or another communications connection (eitherhardwired, wireless, or any combination of hardwired or wireless) to acomputer, the computer properly views the connection as a transmissionmedium. Transmission media can include a network and/or data links,which can be used to carry desired program code means in the form ofcomputer-executable instructions or data structures and which can beaccessed by a general purpose or special purpose computer. Combinationsof the above should also be included within the scope of non-transitorycomputer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause the processor to performa certain function or group of functions. The computer-executableinstructions may be, for example, binaries, intermediate formatinstructions, such as assembly language, or even source code. Althoughthe subject matter has been described in language specific to structuralfeatures and/or methodological acts, it is to be understood that thesubject matter defined in the appended claims is not necessarily limitedto the described features or acts described above. Rather, the describedfeatures and acts are disclosed as example forms of implementing theclaims.

A memory device, such as a memory provided in the computer 146 or in avehicle computer, can include any one memory element or a combination ofvolatile memory elements (e.g., random access memory (RAM, such as DRAM,SRAM, SDRAM, etc.)) and non-volatile memory elements (e.g., ROM, harddrive, tape, CDROM, etc.). Moreover, the memory device may incorporateelectronic, magnetic, optical, and/or other types of storage media. Inthe context of this document, a “non-transitory computer-readablemedium” can be, for example but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device. More specific examples (a non-exhaustive list) of thecomputer-readable medium would include the following: a portablecomputer diskette (magnetic), a random-access memory (RAM) (electronic),a read-only memory (ROM) (electronic), an erasable programmableread-only memory (EPROM, EEPROM, or Flash memory) (electronic), and aportable compact disc read-only memory (CD ROM) (optical). Note that thecomputer-readable medium could even be paper or another suitable mediumupon which the program is printed, since the program can beelectronically captured, for instance, via optical scanning of the paperor other medium, then compiled, interpreted or otherwise processed in asuitable manner if necessary, and then stored in a computer memory.

Those skilled in the art will appreciate that the present disclosure maybe practiced in network computing environments with many types ofcomputer system configurations, including in-dash vehicle computers,personal computers, desktop computers, laptop computers, messageprocessors, personal communication devices, multi-processor systems,microprocessor-based or programmable consumer electronics, network PCs,minicomputers, mainframe computers, mobile telephones, PDAs, tablets,pagers, routers, switches, various storage devices, and the like. Thedisclosure may also be practiced in distributed system environmentswhere local and remote computer systems, which are linked (either byhardwired data links, wireless data links, or by any combination ofhardwired and wireless data links) through a network, both performtasks. In a distributed system environment, program modules may belocated in both the local and remote memory storage devices.

Further, where appropriate, the functions described herein can beperformed in one or more of hardware, software, firmware, digitalcomponents, or analog components. For example, one or more applicationspecific integrated circuits (ASICs) can be programmed to carry out oneor more of the systems and procedures described herein. Certain termsare used throughout the description, and claims refer to particularsystem components. As one skilled in the art will appreciate, componentsmay be referred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

At least some embodiments of the present disclosure have been directedto computer program products comprising such logic (e.g., in the form ofsoftware) stored on any computer-usable medium. Such software, whenexecuted in one or more data processing devices, causes a device tooperate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the presentdisclosure. Thus, the breadth and scope of the present disclosure shouldnot be limited by any of the above-described example embodiments butshould be defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the present disclosure to the precise formdisclosed. Many modifications and variations are possible in light ofthe above teaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the presentdisclosure. For example, any of the functionality described with respectto a particular device or component may be performed by another deviceor component. Further, while specific device characteristics have beendescribed, embodiments of the disclosure may relate to numerous otherdevice characteristics. Further, although embodiments have beendescribed in language specific to structural features and/ormethodological acts, it is to be understood that the disclosure is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as illustrative forms ofimplementing the embodiments. Conditional language, such as, amongothers, “can,” “could,” “might,” or “may,” unless specifically statedotherwise, or otherwise understood within the context as used, isgenerally intended to convey that certain embodiments could include,while other embodiments may not include, certain features, elements,and/or steps. Thus, such conditional language is not generally intendedto imply that features, elements, and/or steps are in any way requiredfor one or more embodiments.

That which is claimed is:
 1. A method comprising: receiving, from afirst vehicle, sensor data comprising a condition of a road; predictinga functional condition of the road based on evaluating the sensor data;determining, based on the functional condition of the road, a level ofsupport offered by the road for autonomous driving operations; andassigning the road a first classification based on the level of supportoffered by the road for autonomous driving operations.
 2. The method ofclaim 1, wherein the condition is a current condition and the functionalcondition is a future condition, and wherein predicting the futurecondition comprises using a deterioration model that is based on aneffect of an environmental condition upon a road surface over a periodof time.
 3. The method of claim 2, wherein the environmental conditionis a weather condition.
 4. The method of claim 1, wherein the level ofsupport offered by the road for autonomous driving operations is basedon road markings, traffic signs, traffic signals, and/or infrastructureelements that support autonomous driving operations.
 5. The method ofclaim 1, further comprising: receiving infrastructure data of the road;and predicting the functional condition of the road based on evaluatingthe sensor data in combination with the infrastructure data.
 6. Themethod of claim 5, wherein the infrastructure data comprises records ofroadwork performed upon the road.
 7. The method of claim 1, furthercomprising: receiving, from a second vehicle, second sensor datacomprising a second functional condition of the road; and predicting thefunctional condition of the road based on evaluating the sensor data incombination with the second sensor data.
 8. A method comprising:predicting a future functional condition of a road based on evaluating acurrent functional condition of the road; determining, based on thefuture functional condition of the road, a level of support offered bythe road for autonomous driving operations; determining an autonomousdriving capability of a first vehicle; and determining, based on thefuture functional condition of the road and the autonomous drivingcapability of the first vehicle, a viability of the first vehicle totravel on the road.
 9. The method of claim 8, wherein evaluating thecurrent functional condition of the road comprises using a deteriorationmodel that is based on an effect of an environmental condition upon aroad surface.
 10. The method of claim 8, further comprising: assigning afirst classification to the road based on evaluating the currentfunctional condition of the road; and changing the first classificationto a second classification based on evaluating the future functionalcondition of the road; and determining, based on the secondclassification, the viability of the first vehicle to travel on theroad.
 11. The method of claim 10, further comprising: determining, basedon the autonomous driving capability of the first vehicle and the firstclassification of the road, that the road is viable for current travelby the first vehicle.
 12. The method of claim 11, further comprising:determining, based on the autonomous driving capability of the firstvehicle and the second classification of the road, that the road is notviable for future travel by the first vehicle.
 13. The method of claim12, further comprising: determining the autonomous driving capability ofa second vehicle; and determining, based on the autonomous drivingcapability of the second vehicle and the second classification of theroad, that the road is viable for future travel by the second vehicle.14. The method of claim 13, wherein evaluating the current functionalcondition of the road comprises evaluating sensor data from the firstvehicle and the second vehicle, and wherein the sensor data isassociated with road markings, traffic signs, traffic signals, and/orinfrastructure elements that support autonomous driving operations. 15.A system comprising: a memory that stores computer-executableinstructions; and a processor configured to execute thecomputer-executable instructions to: receive, from a first vehicle,first sensor data comprising a first functional condition of a road;predict a future functional condition of the road based on evaluatingthe first sensor data; determine, based on the future functionalcondition of the road, a level of support offered by the road forautonomous driving operations; and assign, to the road, a firstclassification based on the level of support offered by the road forautonomous driving operations.
 16. The system of claim 15, whereinevaluating the first sensor data comprises using a deterioration modelthat is based on an effect of an environmental condition upon a roadsurface.
 17. The system of claim 15, wherein evaluating the first sensordata comprises evaluating sensor data associated with road markings,traffic signs, traffic signals, and/or infrastructure elements thatsupport autonomous driving operations.
 18. The system of claim 15,wherein the processor is further configured to: determine an autonomousdriving capability of the first vehicle; and determine, based on thefirst classification of the road and the autonomous driving capabilityof the first vehicle, a viability of the first vehicle to travel on theroad.
 19. The system of claim 18, wherein the processor is furtherconfigured to: determine, based on the autonomous driving capability ofthe first vehicle and the first classification of the road, that theroad is not viable for future travel by the first vehicle.
 20. Thesystem of claim 18, wherein the processor is further configured to:determine the autonomous driving capability of a second vehicle; anddetermine, based on the autonomous driving capability of the secondvehicle and the first classification of the road, that the road isviable for future travel by the second vehicle.